Company Filing History:
Years Active: 2025
Title: Innovations of Nahid Ul Islam in Medical Imaging
Introduction
Nahid Ul Islam is an accomplished inventor based in Mesa, Arizona, known for his significant contributions to the field of medical imaging. With a total of two patents, he has developed innovative systems and methods that enhance the accuracy and efficiency of medical diagnoses.
Latest Patents
Nahid's latest patents include groundbreaking technologies aimed at improving medical image classification and diagnosis. The first patent focuses on "Systems, methods, and apparatuses for implementing self-supervised domain-adaptive pre-training via a transformer for use with medical image classification." This invention describes a system that utilizes non-medical photographic images to pre-train an AI model, which is then fine-tuned with medical images to generate a domain-adapted AI model capable of predicting medical diagnoses from unseen input images.
The second patent, titled "Systems, methods, and apparatuses for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism," outlines a system designed to diagnose pulmonary embolism using new medical images. This system employs a modified CNN architecture with a squeeze and excitation block to enhance feature extraction, ultimately providing accurate predictions regarding the presence of pulmonary embolism in medical images.
Career Highlights
Nahid Ul Islam is affiliated with Arizona State University, where he continues to advance research in medical imaging technologies. His work is characterized by a commitment to improving diagnostic processes through innovative AI applications.
Collaborations
Nahid collaborates with notable colleagues, including Jianming Liang and Shiv Gehlot, who contribute to the development and refinement of his inventions.
Conclusion
Nahid Ul Islam's contributions to medical imaging through his patents reflect his dedication to enhancing healthcare technology. His innovative approaches are paving the way for more accurate and efficient medical diagnoses.